Appearance Defect Detection Method of Aluminum Electrolytic Capacitor Based on YOLOv8
Traditional aluminum electrolytic capacitor quality inspection relies on manual visual inspection to detect defects such as scratches and damages on the casing,and this detection method encounters problems with accuracy and efficiency.To address the problems,this paper proposes and implements a capacitor defect detection scheme based on YOLOv8.By constructing a capacitor defect database and training the YOLOv8 model on defects such as dents,scratches,and damages,the scheme successfully achieves capacitor appearance defect detection function.Experimental data results show that the model's mAP@50 exceeds 87%.Compared to traditional detection methods,the capacitor defect detection scheme based on YOLOv8 has higher accuracy and efficiency.Further construction of a capacitor appearance defect database can enhance detection accuracy and efficiency,providing a feasible solution for defect detection in capacitor industrial production.
万方数据
维普
